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A First Course In Statistical Learning (MAST90104)
Graduate courseworkPoints: 25Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
Availability | Semester 2 - Dual-Delivery |
---|---|
Fees | Look up fees |
Supervised statistical learning is based on the widely used linear models that model a response as a linear combination of explanatory variables. Initially this subject develops an elegant unified theory for a quantitative response that includes the estimation of model parameters, hypothesis testing using analysis of variance, model selection, diagnostics on model assumptions, and prediction. Some classification methods for qualitative responses are then developed. This subject then considers computational techniques, including the EM algorithm. Bayes methods and Monte-Carlo methods are considered. The subject concludes by considering some unsupervised learning techniques.
Intended learning outcomes
On completion of this subject students should be able to:
- Understand the underlying statistical theory of linear models and the limitations of such models.
- Fit linear models to data using a standard statistical computing package and interpret the results.
- Be able to predict (or classify) qualitative responses.
- Understand the underlying theory of Bayesian statistics.
- Understand the underlying theory and be able to apply the EM algorithm in simple settings.
- Be able to use a computer package to perform statistical computing and data analysis
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90105 | Methods of Mathematical Statistics | Semester 1 (Dual-Delivery - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Four written assignments amounting to a total of up to 100 pages. 100pp ≈ 2000 words, due during regular intervals (5% each)
| During the teaching period | 20% |
Written examination
| Week 7 | 35% |
Computer labratory test
| End of the teaching period | 10% |
Written examination
| 35% |
Last updated: 3 November 2022
Dates & times
- Semester 2
Coordinator KD Dang Mode of delivery Dual-Delivery (Parkville) Contact hours 4 x one hour lectures per week, 1 x one hour practice class per week, and 1 x one hour computer laboratory class per week Total time commitment 340 hours Teaching period 26 July 2021 to 24 October 2021 Last self-enrol date 6 August 2021 Census date 31 August 2021 Last date to withdraw without fail 24 September 2021 Assessment period ends 19 November 2021 Semester 2 contact information
Time commitment details
340 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022